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Found 5 papers, 2 papers with code
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion
The task-agnostic prototypes can then be reintegrated to generate a new model that solves a new task encoded with a different prototype distribution.
Verifiably Safe Exploration for End-to-End Reinforcement Learning
We also prove that our method of enforcing the safety constraints preserves all safe policies from the original environment.
Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges
Autonomous systems -- such as self-driving cars, autonomous drones, and automated trains -- must come with strong safety guarantees.
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations.
Probabilistic Federated Neural Matching
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
